Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations299
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.5 KiB
Average record size in memory104.4 B

Variable types

Numeric7
Categorical6

Alerts

DEATH_EVENT is highly overall correlated with timeHigh correlation
time is highly overall correlated with DEATH_EVENTHigh correlation

Reproduction

Analysis started2025-07-18 10:37:18.940379
Analysis finished2025-07-18 10:37:30.586840
Duration11.65 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct47
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.833893
Minimum40
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-18T18:37:30.850934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile42.9
Q151
median60
Q370
95-th percentile82
Maximum95
Range55
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.894809
Coefficient of variation (CV)0.19552931
Kurtosis-0.18487053
Mean60.833893
Median Absolute Deviation (MAD)10
Skewness0.42306191
Sum18189.334
Variance141.48648
MonotonicityNot monotonic
2025-07-18T18:37:31.052904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
60 33
 
11.0%
50 27
 
9.0%
65 26
 
8.7%
70 25
 
8.4%
45 19
 
6.4%
55 17
 
5.7%
75 11
 
3.7%
53 10
 
3.3%
58 10
 
3.3%
63 8
 
2.7%
Other values (37) 113
37.8%
ValueCountFrequency (%)
40 7
 
2.3%
41 1
 
0.3%
42 7
 
2.3%
43 1
 
0.3%
44 2
 
0.7%
45 19
6.4%
46 3
 
1.0%
47 1
 
0.3%
48 2
 
0.7%
49 4
 
1.3%
ValueCountFrequency (%)
95 2
 
0.7%
94 1
 
0.3%
90 3
1.0%
87 1
 
0.3%
86 1
 
0.3%
85 6
2.0%
82 3
1.0%
81 1
 
0.3%
80 7
2.3%
79 1
 
0.3%

anaemia
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
170 
1
129 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters299
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 170
56.9%
1 129
43.1%

Length

2025-07-18T18:37:31.238612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T18:37:31.390667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 170
56.9%
1 129
43.1%

Most occurring characters

ValueCountFrequency (%)
0 170
56.9%
1 129
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 170
56.9%
1 129
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 170
56.9%
1 129
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 170
56.9%
1 129
43.1%

creatinine_phosphokinase
Real number (ℝ)

Distinct208
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean581.83946
Minimum23
Maximum7861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-18T18:37:31.579516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile59
Q1116.5
median250
Q3582
95-th percentile2263
Maximum7861
Range7838
Interquartile range (IQR)465.5

Descriptive statistics

Standard deviation970.28788
Coefficient of variation (CV)1.6676213
Kurtosis25.149046
Mean581.83946
Median Absolute Deviation (MAD)182
Skewness4.4631101
Sum173970
Variance941458.57
MonotonicityNot monotonic
2025-07-18T18:37:31.864513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
582 47
 
15.7%
66 4
 
1.3%
129 4
 
1.3%
231 3
 
1.0%
69 3
 
1.0%
68 3
 
1.0%
84 3
 
1.0%
115 3
 
1.0%
59 3
 
1.0%
60 3
 
1.0%
Other values (198) 223
74.6%
ValueCountFrequency (%)
23 1
 
0.3%
30 1
 
0.3%
47 3
1.0%
52 1
 
0.3%
53 1
 
0.3%
54 1
 
0.3%
55 1
 
0.3%
56 2
0.7%
57 1
 
0.3%
58 1
 
0.3%
ValueCountFrequency (%)
7861 1
0.3%
7702 1
0.3%
5882 1
0.3%
5209 1
0.3%
4540 1
0.3%
3966 1
0.3%
3964 1
0.3%
2794 1
0.3%
2695 1
0.3%
2656 1
0.3%

diabetes
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
174 
1
125 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters299
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 174
58.2%
1 125
41.8%

Length

2025-07-18T18:37:32.058960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T18:37:32.174522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 174
58.2%
1 125
41.8%

Most occurring characters

ValueCountFrequency (%)
0 174
58.2%
1 125
41.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 174
58.2%
1 125
41.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 174
58.2%
1 125
41.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 174
58.2%
1 125
41.8%

ejection_fraction
Real number (ℝ)

Distinct17
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.083612
Minimum14
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-18T18:37:32.285939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile20
Q130
median38
Q345
95-th percentile60
Maximum80
Range66
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.834841
Coefficient of variation (CV)0.31075941
Kurtosis0.04140936
Mean38.083612
Median Absolute Deviation (MAD)8
Skewness0.55538275
Sum11387
Variance140.06346
MonotonicityNot monotonic
2025-07-18T18:37:32.408658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
35 49
16.4%
38 40
13.4%
40 37
12.4%
25 36
12.0%
30 34
11.4%
60 31
10.4%
50 21
7.0%
45 20
6.7%
20 18
 
6.0%
55 3
 
1.0%
Other values (7) 10
 
3.3%
ValueCountFrequency (%)
14 1
 
0.3%
15 2
 
0.7%
17 2
 
0.7%
20 18
 
6.0%
25 36
12.0%
30 34
11.4%
35 49
16.4%
38 40
13.4%
40 37
12.4%
45 20
6.7%
ValueCountFrequency (%)
80 1
 
0.3%
70 1
 
0.3%
65 1
 
0.3%
62 2
 
0.7%
60 31
10.4%
55 3
 
1.0%
50 21
7.0%
45 20
6.7%
40 37
12.4%
38 40
13.4%
Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
194 
1
105 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters299
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 194
64.9%
1 105
35.1%

Length

2025-07-18T18:37:32.607900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T18:37:32.739229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 194
64.9%
1 105
35.1%

Most occurring characters

ValueCountFrequency (%)
0 194
64.9%
1 105
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 194
64.9%
1 105
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 194
64.9%
1 105
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 194
64.9%
1 105
35.1%

platelets
Real number (ℝ)

Distinct176
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263358.03
Minimum25100
Maximum850000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-18T18:37:32.890229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum25100
5-th percentile131800
Q1212500
median262000
Q3303500
95-th percentile422500
Maximum850000
Range824900
Interquartile range (IQR)91000

Descriptive statistics

Standard deviation97804.237
Coefficient of variation (CV)0.37137367
Kurtosis6.2092545
Mean263358.03
Median Absolute Deviation (MAD)44000
Skewness1.4623208
Sum78744051
Variance9.5656687 × 109
MonotonicityNot monotonic
2025-07-18T18:37:33.041368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
263358.03 25
 
8.4%
221000 4
 
1.3%
279000 4
 
1.3%
271000 4
 
1.3%
305000 4
 
1.3%
226000 4
 
1.3%
228000 4
 
1.3%
235000 4
 
1.3%
237000 4
 
1.3%
255000 4
 
1.3%
Other values (166) 238
79.6%
ValueCountFrequency (%)
25100 1
0.3%
47000 1
0.3%
51000 1
0.3%
62000 1
0.3%
70000 1
0.3%
73000 1
0.3%
75000 1
0.3%
87000 1
0.3%
105000 1
0.3%
119000 1
0.3%
ValueCountFrequency (%)
850000 1
0.3%
742000 1
0.3%
621000 1
0.3%
543000 1
0.3%
533000 1
0.3%
507000 1
0.3%
504000 1
0.3%
497000 1
0.3%
481000 1
0.3%
461000 1
0.3%

serum_creatinine
Real number (ℝ)

Distinct40
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3938796
Minimum0.5
Maximum9.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-18T18:37:33.170099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.7
Q10.9
median1.1
Q31.4
95-th percentile3
Maximum9.4
Range8.9
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation1.0345101
Coefficient of variation (CV)0.74218036
Kurtosis25.828239
Mean1.3938796
Median Absolute Deviation (MAD)0.2
Skewness4.4559959
Sum416.77
Variance1.0702111
MonotonicityNot monotonic
2025-07-18T18:37:33.322816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 50
16.7%
1.1 32
10.7%
0.9 32
10.7%
1.2 24
 
8.0%
0.8 24
 
8.0%
1.3 20
 
6.7%
0.7 19
 
6.4%
1.18 11
 
3.7%
1.4 9
 
3.0%
1.7 9
 
3.0%
Other values (30) 69
23.1%
ValueCountFrequency (%)
0.5 1
 
0.3%
0.6 4
 
1.3%
0.7 19
 
6.4%
0.75 1
 
0.3%
0.8 24
8.0%
0.9 32
10.7%
1 50
16.7%
1.1 32
10.7%
1.18 11
 
3.7%
1.2 24
8.0%
ValueCountFrequency (%)
9.4 1
0.3%
9 1
0.3%
6.8 1
0.3%
6.1 1
0.3%
5.8 1
0.3%
5 1
0.3%
4.4 1
0.3%
4 1
0.3%
3.8 1
0.3%
3.7 1
0.3%

serum_sodium
Real number (ℝ)

Distinct27
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.62542
Minimum113
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-18T18:37:33.421252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum113
5-th percentile130
Q1134
median137
Q3140
95-th percentile144
Maximum148
Range35
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.4124773
Coefficient of variation (CV)0.032296167
Kurtosis4.119712
Mean136.62542
Median Absolute Deviation (MAD)3
Skewness-1.048136
Sum40851
Variance19.469956
MonotonicityNot monotonic
2025-07-18T18:37:33.506068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
136 40
13.4%
137 38
12.7%
140 35
11.7%
134 32
10.7%
138 23
7.7%
139 22
 
7.4%
135 16
 
5.4%
132 14
 
4.7%
141 12
 
4.0%
142 11
 
3.7%
Other values (17) 56
18.7%
ValueCountFrequency (%)
113 1
 
0.3%
116 1
 
0.3%
121 1
 
0.3%
124 1
 
0.3%
125 1
 
0.3%
126 1
 
0.3%
127 3
 
1.0%
128 2
 
0.7%
129 2
 
0.7%
130 9
3.0%
ValueCountFrequency (%)
148 1
 
0.3%
146 1
 
0.3%
145 9
 
3.0%
144 5
 
1.7%
143 3
 
1.0%
142 11
 
3.7%
141 12
 
4.0%
140 35
11.7%
139 22
7.4%
138 23
7.7%

sex
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
1
194 
0
105 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters299
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 194
64.9%
0 105
35.1%

Length

2025-07-18T18:37:33.623214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T18:37:33.769311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 194
64.9%
0 105
35.1%

Most occurring characters

ValueCountFrequency (%)
1 194
64.9%
0 105
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 194
64.9%
0 105
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 194
64.9%
0 105
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 194
64.9%
0 105
35.1%

smoking
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
203 
1
96 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters299
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 203
67.9%
1 96
32.1%

Length

2025-07-18T18:37:33.890800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T18:37:34.007907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 203
67.9%
1 96
32.1%

Most occurring characters

ValueCountFrequency (%)
0 203
67.9%
1 96
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 203
67.9%
1 96
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 203
67.9%
1 96
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 203
67.9%
1 96
32.1%

time
Real number (ℝ)

High correlation 

Distinct148
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.26087
Minimum4
Maximum285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-18T18:37:34.157136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile12.9
Q173
median115
Q3203
95-th percentile250
Maximum285
Range281
Interquartile range (IQR)130

Descriptive statistics

Standard deviation77.614208
Coefficient of variation (CV)0.59583671
Kurtosis-1.212048
Mean130.26087
Median Absolute Deviation (MAD)71
Skewness0.12780265
Sum38948
Variance6023.9653
MonotonicityIncreasing
2025-07-18T18:37:34.340805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250 7
 
2.3%
187 7
 
2.3%
10 6
 
2.0%
186 6
 
2.0%
107 6
 
2.0%
30 5
 
1.7%
209 5
 
1.7%
244 5
 
1.7%
95 5
 
1.7%
214 5
 
1.7%
Other values (138) 242
80.9%
ValueCountFrequency (%)
4 1
 
0.3%
6 1
 
0.3%
7 2
 
0.7%
8 2
 
0.7%
10 6
2.0%
11 2
 
0.7%
12 1
 
0.3%
13 1
 
0.3%
14 2
 
0.7%
15 2
 
0.7%
ValueCountFrequency (%)
285 1
 
0.3%
280 1
 
0.3%
278 1
 
0.3%
271 1
 
0.3%
270 2
 
0.7%
258 2
 
0.7%
257 1
 
0.3%
256 2
 
0.7%
250 7
2.3%
247 1
 
0.3%

DEATH_EVENT
Categorical

High correlation 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
203 
1
96 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters299
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 203
67.9%
1 96
32.1%

Length

2025-07-18T18:37:34.454952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T18:37:34.539309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 203
67.9%
1 96
32.1%

Most occurring characters

ValueCountFrequency (%)
0 203
67.9%
1 96
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 203
67.9%
1 96
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 203
67.9%
1 96
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 203
67.9%
1 96
32.1%

Interactions

2025-07-18T18:37:27.949422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:19.359404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:19.640713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:25.764572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:26.418136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:26.854116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:27.370732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:28.046558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:19.397142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:19.682500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:25.814607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:26.475091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:26.921184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:27.454248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:28.296663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:19.440727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:20.929213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:25.886779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:26.549222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:26.998947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:27.552047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:28.466565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:19.476282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:25.099169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:25.948336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:26.604685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:27.070521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:27.651623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:28.767601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:19.512625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:25.565363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:26.015451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:26.670476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:27.154847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:27.715992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:29.015153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:19.552654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:25.633554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:26.087260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:26.721100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:27.218476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:27.799460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:29.275892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:19.595344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:25.696962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:26.152066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:26.799808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:27.298977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T18:37:27.865422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-07-18T18:37:34.602013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
DEATH_EVENTageanaemiacreatinine_phosphokinasediabetesejection_fractionhigh_blood_pressureplateletsserum_creatinineserum_sodiumsexsmokingtime
DEATH_EVENT1.0000.2700.0110.0750.0000.3920.0430.0000.3280.2130.0000.0000.601
age0.2701.0000.139-0.0930.1740.0740.166-0.0520.271-0.1020.0840.000-0.198
anaemia0.0110.1391.0000.0570.0000.0760.0000.0000.0610.0000.0660.0820.050
creatinine_phosphokinase0.075-0.0930.0571.0000.000-0.0680.0000.060-0.0500.0170.0000.0570.126
diabetes0.0000.1740.0000.0001.0000.0000.0000.0730.0000.1090.1390.1280.000
ejection_fraction0.3920.0740.076-0.0680.0001.0000.0000.054-0.1780.1620.1330.0000.071
high_blood_pressure0.0430.1660.0000.0000.0000.0001.0000.1210.0720.0000.0780.0000.223
platelets0.000-0.0520.0000.0600.0730.0540.1211.000-0.0510.0490.1710.026-0.007
serum_creatinine0.3280.2710.061-0.0500.000-0.1780.072-0.0511.000-0.3000.0000.000-0.161
serum_sodium0.213-0.1020.0000.0170.1090.1620.0000.049-0.3001.0000.0850.0000.086
sex0.0000.0840.0660.0000.1390.1330.0780.1710.0000.0851.0000.4350.000
smoking0.0000.0000.0820.0570.1280.0000.0000.0260.0000.0000.4351.0000.070
time0.601-0.1980.0500.1260.0000.0710.223-0.007-0.1610.0860.0000.0701.000

Missing values

2025-07-18T18:37:29.704174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-18T18:37:30.367886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ageanaemiacreatinine_phosphokinasediabetesejection_fractionhigh_blood_pressureplateletsserum_creatinineserum_sodiumsexsmokingtimeDEATH_EVENT
075.005820201265000.001.91301041
155.0078610380263358.031.11361061
265.001460200162000.001.31291171
350.011110200210000.001.91371071
465.011601200327000.002.71160081
590.01470401204000.002.11321181
675.012460150127000.001.213710101
760.013151600454000.001.113111101
865.001570650263358.031.513800101
980.011230351388000.009.413311101
ageanaemiacreatinine_phosphokinasediabetesejection_fractionhigh_blood_pressureplateletsserum_creatinineserum_sodiumsexsmokingtimeDEATH_EVENT
28990.013370380390000.00.9144002560
29045.006151550222000.00.8141002570
29160.003200350133000.01.4139102580
29252.001901380382000.01.0140112580
29363.011031350179000.00.9136112700
29462.00611381155000.01.1143112700
29555.0018200380270000.01.2139002710
29645.0020601600742000.00.8138002780
29745.0024130380140000.01.4140112800
29850.001960450395000.01.6136112850